In this article, we'll explore how to apply Clean Architecture principles when transitioning from a monolithic system to microservices. We'll address common challenges such as:
- Designing adapter layers (for database access and external API calls)
- Handling master data across multiple services
- Dealing with code duplication
- Managing performance impacts when table joins are no longer straightforward
- Planning a phased approach to migrate from a monolith
Whether you're starting fresh with microservices or modernizing a legacy system, these insights will help you build a flexible, maintainable architecture.
1. Clean Architecture × Microservices Overview
1.1 What Is Clean Architecture?
Clean Architecture emphasizes separating business logic (the application core) from external elements (databases, UI, external APIs). At its heart:
- Application Core (Use Cases, Entities): Encapsulates the essential business rules.
- Adapters (or Interface/Infrastructure Layer): Handles external operations (DB queries, API calls, I/O) while shielding the core from infrastructure details.
1.2 What Is Microservices Architecture?
Microservices break a system into independently deployable services, each owning its own data store. Communication occurs via network calls (REST, gRPC, messaging, etc.). This approach increases development agility and scalability but also introduces concerns about data consistency, service orchestration, and operational overhead.
1.3 Why Combine the Two?
- Clean Architecture: Strengthens each microservice internally so that core business logic remains independent of infrastructure details.
- Microservices: Allows teams to scale and release features at the service level.
By mixing Clean Architecture with microservices, you maintain loose coupling and high scalability, while preserving clarity in your domain logic.
2. The Adapter Layer — DB Access & External API Calls
2.1 Role of the Adapter Layer
In Clean Architecture, you avoid pushing external dependencies directly into the core. Instead, all external I/O, such as database queries or API requests, happens in an adapter layer. The core interacts only with abstract interfaces (ports), making it easy to switch implementations.
2.2 DB Access Adapter
- Each microservice typically manages its own database.
- The adapter handles all CRUD operations, encapsulating details like SQL or ORM usage.
- The core (use cases) relies on an interface that the DB adapter implements.
2.3 External API Integration Adapter
- For cross-service or third-party API calls, the adapter manages protocols, request/response formats, and error handling (e.g., retries, circuit breakers).
- This approach keeps the core’s business logic independent of external service details.
3. Master Data Handling — Turning Common Data into a Separate Service
3.1 What Is a Master Data Service?
When multiple services need the same reference data (e.g., country lists, category definitions), you can centralize it in a Master Data Service.
- Ensures consistent data usage across services
- Simplifies updates by localizing changes to one service
3.2 Pros and Cons
-
Pros
- Strong data consistency
- Single point for updates
-
Cons
- Potential single point of failure if not designed with redundancy
- Increased latency from external API calls
Mitigation Tips:
- Use caching (e.g., Redis) to avoid excessive calls.
- Implement circuit breakers and retries for resilience.
4. Code Duplication — Balancing DRY and Service Independence
4.1 The DRY Principle vs. Microservice Independence
- Ideally, you reduce duplication by placing common logic into libraries or SDKs.
- However, microservices often deliberately allow some duplication to keep services truly decoupled, each evolving at its own pace.
4.2 Minimizing Duplication
-
Create a Common Library
- Distribute shared API client code, data transformation logic, or utility functions.
-
Use an API Gateway or Service Mesh
- Centralize cross-cutting concerns like authentication, routing, or retries.
-
Leverage Shared Caching Strategies
- Provide a standardized approach so each service doesn’t reimplement the same patterns.
Table Example:
| Method | Pros | Cons |
|---|---|---|
| Common Library (SDK) | Easy to apply DRY; single source of truth | Version conflicts across services |
| API Gateway | Simplifies client requests; reduces overhead | Gateway can become a single point of failure |
| Intentional Duplication | Each service can evolve independently | Fixes/updates must be applied in multiple places |
5. Dealing with the Loss of Table Joins & Performance Impacts
5.1 From Monolith to Distributed Data
In a monolith, you can easily use JOIN queries. In microservices, each service owns its own data, making direct joins impossible. This demands alternative solutions for queries spanning multiple services.
5.2 Data Integration Patterns
-
CQRS (Command Query Responsibility Segregation)
- Separate read models from write models.
- Maintain a read-optimized view (materialized view or “query side”) using events or batch updates.
-
Data Aggregation Service
- A specialized service that fetches data from multiple services, combines it, and returns a single response.
- Frontend sees only one endpoint.
-
Caching
- Use Redis or in-memory caches to store frequently accessed, aggregated data for faster responses.
6. A Phased Migration from Monolith to Microservices
6.1 The Strangler Fig Pattern
Gradually replace parts of the legacy monolith with new microservices, leaving the old system running until it’s safe to switch over.
6.2 Step-by-Step Approach
-
API-Enable the Monolith
- Refactor direct DB calls into internal APIs.
-
Redirect to New Services
- As you introduce microservices, route specific functionality from the monolith to the new system.
-
Gradual Cutover
- Retire monolith components in stages, once the new services are stable and fully tested.
7. Summary & Key Takeaways
-
Apply Clean Architecture
- Keep your core business logic separate from infrastructure details with a well-defined adapter layer.
-
Centralize Common Data
- If multiple services rely on the same data, consider a Master Data Service plus caching and fault tolerance strategies.
-
Balance Code Duplication
- Be mindful of DRY but accept that some duplication may keep services independent and easier to maintain.
-
Address JOIN Limitations
- Adopt patterns like CQRS or a data aggregation service to replicate the convenience of table joins without merging databases.
-
Migrate in Phases
- Use the Strangler Fig pattern to introduce microservices incrementally, reducing risk and ensuring steady progress.
By combining microservices with Clean Architecture, you gain a scalable, maintainable solution that keeps domain logic clean. While you lose the simplicity of monolithic table joins, you gain flexibility, autonomy for each service, and the potential for more robust, scalable applications over the long run.
References
- The Clean Architecture (by Uncle Bob) https://8thlight.com/blog/uncle-bob/2012/08/13/the-clean-architecture.html
- Microservices Patterns (by Chris Richardson) https://microservices.io/patterns/index.html
- Strangler Fig Pattern (Martin Fowler) https://martinfowler.com/bliki/StranglerFigApplication.html
- CQRS (Command Query Responsibility Segregation) – Martin Fowler https://martinfowler.com/bliki/CQRS.html
Top comments (0)